IROS

Real-Time Reinforcement Learning for Dynamic Tasks with a Parallel Soft Robot

动态任务的实时强化学习与并行软机器人

James Avtges, Jake Ketchum, Millicent Schlafly, Helena Young, Taekyoung Kim, Allison Pinosky, Ryan L. Truby, Todd D. Murphey

机构 * Department of Mechanical Engineering, Northwestern University（西北大学机械工程系） ； Department of Materials Science and Engineering, Northwestern University（西北大学材料科学与工程系） 纠错

AI总结 本文提出基于课程学习的实时强化学习方法，用于在单次部署中实现软机器人的动态平衡，通过并行软执行器和HSA结构实现高可靠性控制。

Comments Published at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2025